Practical Time Series Analysis

Start Date: 07/05/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/practical-time-series-analysis

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About Course

Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!

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Course Introduction

Practical Time Series Analysis This course covers the most important aspects of constructing graphical representations of computer-time data. We cover topics such as creating logical sections of time series data; creating the most logical aggregations; and exploring and visualizing the data. We’ll also cover the exacting art of constructing bar plots and exploring the relationship between data and time.Practical Logical Section Exploring Bar Chart Formulas Exploring Bars and Time Series Data and Their Relationships Power Onboarding Power Onboarding is a course on how to help someone leave a dangerous job or meet new responsibilities. We cover a wide variety of topics, including skills for starting as new employees, things to consider when you're interviewing, communication strategies, and personal safety. We’ll also cover things to consider when you're on the phone with your boss, things to consider when you're driving, and things to consider when you're dealing with the legal system. We’ll also cover things to consider when you're dealing with the emergency vehicle and medical services, things to consider when you're dealing with maintenance issues, and things to consider when you're dealing with the rest of your life. There are no required skills for taking this course, but we do encourage you to try and think critically. You’ll learn a lot about yourself and other people, and about how they’re dealing with their own life

Course Tag

Time Series Forecasting Time Series Time Series Models

Related Wiki Topic

Article Example
Time series Time series "analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series "forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called "time series analysis", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.
Time–frequency analysis An early practical motivation for time–frequency analysis was the development of radar – see ambiguity function.
Time series Time series metrics or features that can be used for time series classification or regression analysis:
Time series In addition, time-series analysis can be applied where the series are seasonally stationary or non-stationary. Situations where the amplitudes of frequency components change with time can be dealt with in time-frequency analysis which makes use of a time–frequency representation of a time-series or signal.
Time series Methods for time series analysis may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In the time domain, correlation and analysis can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in the frequency domain.
Time series Methods of time series analysis may also be divided into linear and non-linear, and univariate and multivariate.
Time–frequency analysis The practical motivation for time–frequency analysis is that classical Fourier analysis assumes that signals are infinite in time or periodic, while many signals in practice are of short duration, and change substantially over their duration. For example, traditional musical instruments do not produce infinite duration sinusoids, but instead begin with an attack, then gradually decay. This is poorly represented by traditional methods, which motivates time–frequency analysis.
Time series A number of different notations are in use for time-series analysis. A common notation specifying a time series "X" that is indexed by the natural numbers is written
Time series Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)
Time series There are several types of motivation and data analysis available for time series which are appropriate for different purposes and etc.
Directional symmetry (time series) In statistical analysis of time series and in signal processing, directional symmetry is a statistical measure of a model's performance in predicting the direction of change, positive or negative, of a time series from one time period to the next.
Unevenly spaced time series most of the basic theory for time series analysis was developed at a time when limitations in computing resources favored an analysis of equally spaced data, since in this case efficient linear algebra routines can be used and many problems have an explicit solution. As a result, fewer methods currently exist specifically for analyzing unevenly spaced time series data.
Time series In the context of statistics, econometrics, quantitative finance, seismology, meteorology, and geophysics the primary goal of time series analysis is forecasting. In the context of signal processing, control engineering and communication engineering it is used for signal detection and estimation, while in the context of data mining, pattern recognition and machine learning time series analysis can be used for clustering, classification, query by content, anomaly detection as well as forecasting.
Decomposition of time series This is an important technique for all types of time series analysis, especially for seasonal adjustment. It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behaviour. For example, time series are usually decomposed into:
Smoothed analysis Smoothed analysis is a way of measuring the complexity of an algorithm. It gives a more realistic analysis of the practical performance of the algorithm, such as its running time, than using worst-case or average-case scenarios.
Time series Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language).
Time series Working with Time Series data is a relatively common use for statistical analysis software. As a result of this, there are many offerings both commercial and open source. Some examples include:
Time series Non-linear dependence of the level of a series on previous data points is of interest, partly because of the possibility of producing a chaotic time series. However, more importantly, empirical investigations can indicate the advantage of using predictions derived from non-linear models, over those from linear models, as for example in nonlinear autoregressive exogenous models. Further references on nonlinear time series analysis: (Kantz and Schreiber), and (Abarbanel)
Decomposition of time series The theory of time series analysis makes use of the idea of decomposing a times series into deterministic and non-deterministic components (or predictable and unpredictable components). See Wold's theorem and Wold decomposition.
Gradient pattern analysis When GPA is conjugated with wavelet analysis, then the method is called "Gradient spectral analysis" (GSA), usually applied to short time series analysis.